What is Anonymous (Lambda)?

What is Anonymous (Lambda) Functions in Python with syntax and example.

Lambda functions are used for creating short, simple operation functions and are useful when you need a quick function for a one-time use.

lambda functions are also called anonymous functions because they don’t have a name like regular functions.

Syntax:

lambda arguments: expression

• Arguments: the parameters used within the lambda expression.
• Expression: Operation or functionality of the function.

Example: Create a function to add two numbers using the lambda function.
Solution:

sumthem = lambda a, b : print(a+b)

sumthem(10, 20)

Output:

30

 Example: example that uses a lambda function to double a number:
Solution:

double = lambda x: x * 2
print(double(5)) 

Output:

10

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How to apply Conditional Expressions in lambda function.

Lambda functions can be useful for small conditional expressions in data structures.

Syntex:

lambda arguments : "success statment" if condation else "fail statment"

Example:

get_status = lambda x: "Pass" if x >= 50 else "Fail"
print(get_status(70)) 

Output:

Pass

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What are the use cases of lambda functions in Python?

Here are some common use cases:

1. map()
2. filter()
3. reduce()

How to use map() function with lambda?

map() applies a function to every item in an iterable (e.g., a list) and returns a new object. It basically helps to modify data.

Syntax:

map(lambda_function, data)

Example:

numbers = [1, 2, 3, 4]
doubled = map(lambda x: x * 2, numbers)
print(doubled) 

Note : Always remember map() function always return the output in the object format, so you have to do type casting to get the data.

 Output:

<map object at 0x7b1327fd9ab0>

so:

numbers = [1, 2, 3, 4]
doubled = map(lambda x: x * 2, numbers)
print(list(doubled)) 

Output:

[2, 4, 6, 8]

 

How to use filter() function with lambda?

filter() returns items in an iterable that match a condition.

Syntax:

filter(lambda_function, dataset)

Example:

numbers = [1, 2, 3, 4, 5, 6]
even_numbers = filter(lambda x: x % 2 == 0, numbers)
print(list(even_numbers)) 

Note : Always remember filter() function always return the output in the object format, so you have to do type casting to get the data.

 Output:

[2, 4, 6]

 

 How to use reduce() function with lambda?

reduce() is a function from functools that applies a rolling computation to items in an iterable.

Syntax:

from functools import reduce

reduce(lambda_function, dataset)

Example:

from functools import reduce
numbers = [1, 2, 3, 4]
product = reduce(lambda x, y: x * y, numbers)
print(product) 

 Output:

24

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Unique interview questions covering lambda functions, map(), filter(), reduce(),

Question 1. How can you use map() with multiple iterables and a lambda function?

Answer: The map() function can process multiple iterables if the lambda function takes more than one argument. Each iterable provides one argument to the lambda function in sequence.

Example:

 

nums1 = [1, 2, 3]
nums2 = [4, 5, 6]
result = list(map(lambda x, y: x + y, nums1, nums2))
print(result) 

Output:

[5, 7, 9]

 

Question 2. Explain how to use filter() with multiple conditions in a lambda function.

Answer: You can combine multiple conditions using logical operators like and, or in a lambda function within filter().

Example:

nums = [10, 15, 20, 25, 30]
result = list(filter(lambda x: x % 2 == 0 and x > 15, nums))
print(result) 

Output:

[20, 30]

 

 Question 3. Can a lambda function inside reduce() keep track of the previous result? How?

Answer: Yes, reduce() naturally passes the cumulative result (or “accumulator”) to the lambda function, allowing it to "remember" previous results.

Example:

from functools import reduce
nums = [1, 2, 3, 4]
result = reduce(lambda acc, x: acc + x, nums)
print(result)

Output:

10

 

 Question 4.  How would you use map() to apply different operations on elements from two lists?

Answer: You can use map() with a lambda that conditionally applies different operations based on element values.

Example:

nums1 = [10, 20, 30]
nums2 = [1, 2, 3]
result = list(map(lambda x, y: x * y if x > y else x + y, nums1, nums2))
print(result) 

Output:

[10, 40, 90]

 

 Question 5. How can you use map() with a lambda function that returns multiple values for each element?

Answer: A lambda function can return a tuple or a list for each element, creating multiple outputs per element.

Example:

nums = [1, 2, 3]
result = list(map(lambda x: (x, x**2), nums))
print(result)

Output:

[(1, 1), (2, 4), (3, 9)]

 

 Question 6. How can lambda functions with filter() be used to filter out None values?

Answer: By using a lambda function to filter out elements that are not None.

Example:

values = [None, 'hello', None, 'world']
result = list(filter(lambda x: x is not None, values))
print(result) 

Output:

['hello', 'world']

 

 Question 7. Explain how you would use a lambda function within map() to flatten a list of lists.

Answer: Use a lambda function to unpack each inner list and return its elements as single values.

Example:

lists = [[1, 2], [3, 4], [5, 6]]
result = list(map(lambda x: x[0] + x[1], lists))
print(result) 

Output:

[3, 7, 11]

 

 Question 8. How can map() and filter() be used together with a lambda to process data?

Answer: Use filter() first to select items based on a condition, then map() to transform the selected items.

Example:

nums = [1, 2, 3, 4, 5, 6]
result = list(map(lambda x: x * 2, filter(lambda x: x % 2 == 0, nums)))
print(result)  

Output:

[4, 8, 12]

 

 Question 9. Can you use lambda functions with map() to process elements differently based on index position?

Answer: Yes, by using enumerate() with map() to include the index in the lambda function.

Example:

nums = [10, 20, 30]
result = list(map(lambda i_n: i_n[0] * i_n[1], enumerate(nums)))
print(result)  

Output:

[0, 20, 60]

 

 Question 10. How do you use reduce() to find the longest string in a list?

Answer: reduce() can be used with a lambda to compare lengths and keep the longest string.

Example:

from functools import reduce
words = ["apple", "banana", "cherry"]
longest = reduce(lambda acc, x: x if len(x) > len(acc) else acc, words)
print(longest)

Output:

banana

 

 Question 11. How can you create a dictionary from two lists using map() and lambda?

Answer: Use map() with zip() to create key-value pairs and then convert them to a dictionary.

Example:

keys = ['a', 'b', 'c']
values = [1, 2, 3]
result = dict(map(lambda x_y: (x_y[0], x_y[1]), zip(keys, values)))
print(result) 

Output:

{'a': 1, 'b': 2, 'c': 3}

 

 Question 12. How do you use filter() to extract only uppercase words from a list?

Answer: Use filter() with a lambda function that checks if each word is uppercase.

Example:

words = ["Hello", "WORLD", "Python", "CODING"]
result = list(filter(lambda x: x.isupper(), words))
print(result)  

Output:

['WORLD', 'CODING']

 

 Question 13. How can you use reduce() to count occurrences of each element in a list?

Answer: You can use reduce() with a lambda function to update a dictionary with counts.

Example:

from functools import reduce
items = ['a', 'b', 'a', 'c', 'b', 'a']
counts = reduce(lambda acc, x: acc.update({x: acc.get(x, 0) + 1}) or acc, items, {})
print(counts)  

Output:

{'a': 3, 'b': 2, 'c': 1}